Determining non-cigarette tobacco, alcohol, and substance use typologies across menthol and non-menthol smokers using latent class analysis

Table 1 Optimal number of latent classes: assessment statistics

Model fit
Latent classes	Number of free parameters	LL	BIC	Sample size adjusted BIC	LMR p-value	VLMR LRT p-value	Entropy
1	7	−4353.9	8757.3	8735.1	-	-	-
2	15	−4024.9	8155.9	8108.2	<0.0001	<0.0001	0.80
3	23	−3989.1	8140.9	8067.8	<0.0001	<0.0001	0.91
4	31	−3977.9	8175.0	8076.5	0.17	0.18	0.87
5	39	−3967.9	8211.6	8087.7	0.43	0.44	0.83
Odds of correct classification
	Class 1	Class 2	Class 3	Class 4	Class 5
1	∞
2	12.5	22.8
3	12.0	8.1	54.6
4	3.0	8.6	4.1	40.7
5	3.2	4.1	11.2	4.0	24.0

Note. LL log likelihood, BIC Bayesian information criteria, LMR Lo-Mendell-Rubin, VLMR Vuong-Lo-Mendell-Rubin, LRT likelihood ratio test for k (H0) versus k-1 classes. Odds of correct classification (OCC) > 5 indicates a model with good latent class separation (Collins & Lanza, p. 74); OCC = ∞ indicates perfect classification

ISSN: 1617-9625